Whole-saliva Proteolysis and Its Impact on Salivary Diagnostics
Bibliographic record
Abstract
There is growing interest in the use of human whole saliva for diagnostics and disease monitoring as an alternative to blood samples. In contrast to blood, whole saliva is a non-sterile body fluid. Proper hand-ling and storage are required to preserve the integrity of potential biomarkers. We investigated salivary autoproteolytic degradation using a variety of approaches. We determined inhibition of protease activities by monitoring the endogenous proteome. In addition, the stability of highly protease-susceptible proteins-histatin 5, statherin, and PRP1-was assessed. Experimental variables included (a) protease inhibitors, (b) salivary pH, (c) incubation temperatures, and (d) sample heating. A cocktail containing AEBSF, aprotinin, pancreatic trypsin inhibitor, leupeptin, antipain, and EDTA could not prevent histatin 5, statherin, or PRP1 degradation in whole saliva. Among the other treatments evaluated, short-term storage of freshly collected samples on ice was effective without interfering with the chemistry of the proteome. In conclusion, whole saliva contains a unique mixture of enzymes as evidenced from their resilience to protease inhibition. Analytical evidence on protein stability is needed to ensure the validity of salivary biomarker study outcomes. Analysis of the data presented will provide help and guidance for the use of saliva samples for diagnostic purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".